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KODAMA: Knowledge Discovery by Accuracy Maximization

An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.

Version: 2.4.1
Depends: R (≥ 2.10.0), stats, minerva, Rtsne, umap
Imports: Rcpp (≥ 0.12.4)
LinkingTo: Rcpp, RcppArmadillo
Suggests: rgl, knitr, rmarkdown
Published: 2024-11-05
DOI: 10.32614/CRAN.package.KODAMA
Author: Stefano Cacciatore ORCID iD [aut, trl, cre], Leonardo Tenori ORCID iD [aut]
Maintainer: Stefano Cacciatore <tkcaccia at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: KODAMA results

Documentation:

Reference manual: KODAMA.pdf
Vignettes: Knowledge Discovery by Accuracy Maximization (source, R code)

Downloads:

Package source: KODAMA_2.4.1.tar.gz
Windows binaries: r-devel: KODAMA_2.4.1.zip, r-release: KODAMA_2.4.1.zip, r-oldrel: KODAMA_2.4.1.zip
macOS binaries: r-release (arm64): KODAMA_2.4.1.tgz, r-oldrel (arm64): KODAMA_2.4.1.tgz, r-release (x86_64): KODAMA_2.4.1.tgz, r-oldrel (x86_64): KODAMA_2.4.1.tgz
Old sources: KODAMA archive

Reverse dependencies:

Reverse depends: MetChem

Linking:

Please use the canonical form https://CRAN.R-project.org/package=KODAMA to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.